According to statistics, coronary heart disease (a full name is coronary atherosclerotic heart disease) is one of major diseases seriously impacting on human health, and its incidence has been increased in recent years. Therefore, it is of great importance to detect, diagnose and treat the coronary heart disease early. The early detection of the coronary heart disease mainly depends on a three-dimensional cardiac image of a patient. Therefore, a method for obtaining the three-dimensional cardiac image of the patient is critical in the early detection of the coronary heart disease.
The heart, which acts as a power source of a human body's circulatory system, is one of the most important organs of the human body. The heart is located within the chest, above the diaphragm and between the two lungs, with the front of the heart neighboring the anterior wall of the chest. Therefore, adjacent tissues of the heart, such as air, the lungs, the diaphragm, a descending aorta, a pulmonary artery, a pulmonary vein and an auricular appendix, are required to be removed from the three-dimensional cardiac image to display the heart, an ascending aorta and a coronary visually clearly, so that the morphology of the ascending aorta, coronary tree and chambers can be checked conveniently.
At present, there are several three-dimensional cardiac image segmentation methods. The most common method is a segmentation method based on traditional region growing. In the region growing segmentation method, the affects of noise and morphology of the neighboring tissues of the heart are not fully taken into account, hence over-segmentation or under-segmentation is prone to occur, which may lead to low accuracy of the segmentation. For example, a left anterior descending coronary artery which is adjacent to the sternum is often removed by using the region growing segmentation method. As another example, a bottom of the heart is connected with the diaphragm, and the boundary between the heart and the diaphragm is not easily distinguishable, therefore the diaphragm is difficult to be removed completely by using the region growing segmentation method, which may affect the observation of a right coronary artery and a posterior descending branch.
Besides the region growing segmentation method, a common method is a training-based segmentation method with higher accuracy compared with the conventional region growing segmentation method. In this segmentation method, a large amount of three-dimensional cardiac data are used for repeat training to obtain an original three-dimensional heart model, and then some processing such as deformation, smoothing processing or segmentation processing are performed on the original three-dimensional heart model to obtain a three-dimensional cardiac image. This segmentation method depends on the original three-dimensional heart model, and the establishment of the model must take repeated training, therefore, the computation amount and complexity of segmentation are increased, and it is difficult to achieve fast image segmentation.
Therefore, it is highly required an effective three-dimensional cardiac image segmentation scheme, to improve the segmentation accuracy, or reduce the computation amount and complexity of segmentation, thereby enabling fast image segmentation.